Learn it Up - Logs

12/05/2026:

• Renamed repo to learn-it-up (and set up a redirection repo page on tcc-learn-it-up)
• Startd trying to run original dance dance convolution repository, but failed once realized that the project uses both tensorflow and python 2, which is a pain in the ass which I don’t want to deal with right now. I’ll be running instead Dance Dance Conv LSTM aka DDCL.

13/05/2026:

• Watched 3b1b (best yt channel) videos on transformers and attention mechanism to get a refresh of the general idea.

14/05/2026:

• I’ve read Attention is All You Need to understand some details glossed over by the 3b1b video and get references for other important topics of which I need to review.

15/05/2026:

• I’ve read Deep Residual Learning for Image Recognition to understand residuals which are used in Attention is All You Need.
• I’ve read Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift to understand batch normalization which is used in Attention is All you Need.

IDEA: Consider training LSTM models using recurrent batch normalization and residual connections in LSTMs To consider this, must search on existing applications of these to LSTMs in other papers to consider results.

16/05/2026:

• Reworked website UI

18/05/2026:

• I’ve read Sequence to Sequence Learning with Neural Networks.

QUESTION: DanceDanceConv and DanceDanceConvLSTM both use an architecture simular to the encoder-decoder. Could doing something like reversing the input possibly improve the model like it does with seq2seq?

• I’ve read Neural Machine Translation by Jointly Learning to Align and Translate to learn the original attention mechanism proposal for RNNs

QUESTION: Could adding this attention encoder-decoder mechanism to DanceDanceConvolution help increase performance without adding the computational costs of ConvLSTM?

19/05/2026:

• I’ve read Attention is All you need once again to understand the main differences between its attention approach than Learning to Align and Translate.

QUESTION: Honestly, the original approach is much simplier, but I see the appeal of the transformer. I wonder if you would get great performance in RNN attention based networks by adding residuals and/or normalization. Food for thought.

• I’ve started re-studying the DanceDanceConvLSTM paper. Altough my original plans were to implement a DanceDanceConvolution like network for computational reasons,

I’ve noticed a detail that I have glossed over in DanceDanceConvLSTM, that is the fact that it does utilize music information when doing step placement, which feels right with me. I shall also inspect the transformer based generation to determine if they do that.